Deep Convolutional Neural Networks (Deep CNNs) are the heart of the remarkable development in deep learning. CNNs have already been used in the 90s to solve the problem of character recognition, but what has become as widespread as it is now is thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, winning other competitors. Then, the convolutional neural network became a very useful tool in the field of machine learning.
Image segmentation, on the other hand, takes a training image or a test image as input and produces a label image as an output. The deep learning has recently become popular, and segmentation is also using the deep learning.
On the other hand, when CNN is learned by a learning device in order to detect one or more obstacles in autonomous driving situations, the learning device must learn various objects to be encountered in the autonomous driving situations. For this purpose, “images for learning”, e.g., images to be used for learning CNN, should be images in which various objects to be encountered in various autonomous driving circumstances are included.
However, in actual driving situations, various objects may exist on the road, but collecting such data on the various objects is not easy. In other words, specific images of specific objects that do not appear frequently on the road will not be easily obtained among general driving image data. For example, since a person, a bicycle, a vehicle, etc. are general images that can be easily obtained among the general driving image data, the learning process for improving detection performance using the general images can be easily performed. However, some uncommon images, i.e., the specific objects, such as a tiger and a crocodile cannot be easily obtained among the general driving image data, and thus the learning process for improving detection performance using the specific objects is not easy.